CN113238486A - Self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method - Google Patents

Self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method Download PDF

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CN113238486A
CN113238486A CN202110619636.4A CN202110619636A CN113238486A CN 113238486 A CN113238486 A CN 113238486A CN 202110619636 A CN202110619636 A CN 202110619636A CN 113238486 A CN113238486 A CN 113238486A
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parameter
parameters
value
iteration
speed regulator
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CN113238486B (en
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栾春林
董金良
韩树军
仝帆
种法政
田浩杰
闫林
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Xinjiang Hami Pumped Storage Co ltd
State Grid Corp of China SGCC
State Grid Xinyuan Co Ltd
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Xinjiang Hami Pumped Storage Co ltd
State Grid Corp of China SGCC
State Grid Xinyuan Co Ltd
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    • G—PHYSICS
    • G05—CONTROLLING; REGULATING
    • G05B—CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS
    • G05B13/00—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion
    • G05B13/02—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric
    • G05B13/04—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators
    • G05B13/042—Adaptive control systems, i.e. systems automatically adjusting themselves to have a performance which is optimum according to some preassigned criterion electric involving the use of models or simulators in which a parameter or coefficient is automatically adjusted to optimise the performance
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
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Abstract

A self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method comprises the following steps: 1. collecting required parameters; 2. constructing a loss model for controlling a hydropower station speed regulator of a plurality of machines; 3. initializing relevant parameters; 4: updating the proportional parameter, the integral parameter and the differential parameter; 5. inputting the calculated proportional parameter, integral parameter and differential parameter into the current system for simulation, collecting system related parameters and calculating the loss value of the current iteration; 6. judging whether the loss value of the current iteration reaches the iteration stop standard, if so, entering the step 7; otherwise, entering a step 8; 7. determining whether to output a parameter result obtained by the iteration of the current round according to the judgment factor, and if not, entering a step 8; 8. and updating the temperature parameters and returning to the step 4. The invention greatly reduces the dependence of the optimization of the parameters of the multi-machine speed regulator on the set initial value by optimizing the model, the parameter updating and the local optimal value, and improves the updating speed and the accuracy of the parameters.

Description

Self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method
Technical Field
The invention belongs to the field of hydroelectric power generation, and particularly relates to a self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method.
Background
When the power demand of the hydropower station changes, the output frequency of a unit in the hydropower station needs to be recovered to 50Hz within the shortest time through a speed regulator in PID control. Because the units in the hydropower station are generally associated and mutually influenced, when the parameters of the speed regulator are regulated and optimized, the accuracy and the robustness of an algorithm only need to be considered, and the stability of the system is ensured by considering the dynamic and static performances of each unit.
In the prior art, parameter regulation and control are mostly carried out on a single-machine speed regulator, and regulation and control of multiple machines mainly depend on a genetic algorithm, a pole allocation method, a particle swarm algorithm and the like. The genetic algorithm has large calculation amount, long required time and large dependence on set parameters. The pole allocation rule has higher requirements on the used model, and the convergence and optimization efficiency is greatly influenced by the initial value setting. Although the particle swarm algorithm has a higher speed compared with the genetic algorithm, premature convergence is easy to fall into local optimum, and the optimization rate is insufficient and the accuracy is not high due to the loss of diversity.
Disclosure of Invention
In order to overcome the defects in the prior art, the invention aims to provide a self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method.
The invention adopts the following technical scheme that a self-adaptive multi-machine hydropower station speed regulator parameter regulation and control method comprises the following steps:
step 1: collecting relevant parameters of each unit in a hydropower station power system;
step 2: constructing a loss model for controlling a hydropower station speed regulator of a plurality of machines;
and step 3: initializing parameters to be regulated and controlled in each unit, and corresponding upper and lower bounds of the parameters and system temperature parameters; the parameters to be regulated comprise a proportional parameter, an integral parameter and a differential parameter;
and 4, step 4: updating the proportional parameter, the integral parameter and the differential parameter;
and 5: inputting the proportional parameter, integral parameter and differential parameter calculated in the step 4 into the current system for simulation, collecting the relevant parameters of the system and calculating the loss value C (X) of the current iterationt；
Step 6: judging whether the loss value of the current iteration reaches the iteration stop standard, if so, entering the step 7; otherwise, entering a step 8;
and 7: randomly generating a judgment factor r from the uniform distribution, and if r meets the local optimization selection condition, outputting a parameter result obtained by the iteration in the current round, namely a final result of parameter regulation and control of the multi-machine hydropower station speed regulator by the method; otherwise, entering a step 8;
and 8: and updating the temperature parameters and returning to the step 4.
In step 1, relevant parameters of each unit comprise output frequency of each unit, signal energy of a control signal, system undershoot and establishment time; the settling time refers to the time required by the unit from outputting an oscillating signal to the time when the signal tends to be stable, and the stability starting time is defined as the time when the amplitude change of the system output signal is maintained between 2% and 5%.
In step 2, the loss model is:
C(X)＝w1×SE+w2×SCS+w3×MU+w4×ST+w5×PI
wherein C (X) represents a loss value for controlling N units, w1、w2、w3、w4And w5Weights corresponding to SE, SCS, MU, ST and PI respectively;
SE represents the sum of the difference between the output frequency of the N units and the reference frequency of 50 Hz;
SCS represents the square sum of the control signals of the N units, namely the signal energy of the control signals of the N units;
MU represents the sum of the N set undershoots;
ST represents the sum of the establishment time of N units;
PI represents the penalty value of N teams to prevent overshoot.
w1Has a value range of (0, 1)]，w2Is selected to be in the range of [500,1500 ]]，w3Is selected in the range of [50,150%]，w4Is selected to be in the range of [1000,5000]，w5Is [1000, ∞).
In step 3, the method for initializing the proportional parameter, the integral parameter and the differential parameter of each system can be random number generation, and can also initialize the parameter of each system by using Gaussian distribution; the values of the proportional parameter, the integral parameter and the differential parameter of each initialized system are required to be between the corresponding upper and lower limits, and the upper limit is required to be larger than the lower limit.
In step 4, the updating method of the parameters comprises the following steps:
Figure BDA0003099046630000021
Figure BDA0003099046630000022
Figure BDA0003099046630000031
wherein the content of the first and second substances,
Figure BDA0003099046630000032
the value of the proportional parameter after the current iteration is represented,
Figure BDA0003099046630000033
representing the value of the scale parameter obtained after the last iteration,
Figure BDA0003099046630000034
the values of the integrated parameters after this iteration are represented,
Figure BDA0003099046630000035
representing the value of the scale parameter obtained after the last iteration,
Figure BDA0003099046630000036
the value of the differential parameter after this iteration is represented,
Figure BDA0003099046630000037
representing the differential parameter value obtained after the last iteration;
t represents the number of iterations, and the initial value is 1; then
Figure BDA0003099046630000038
The proportional parameter of the nth unit is shown,
Figure BDA0003099046630000039
represents the integral parameter of the nth unit,
Figure BDA00030990466300000310
representing a differential parameter of the nth unit;
Figure BDA00030990466300000311
respectively the intermediate quantities of the proportional parameter, the integral parameter and the differential parameter of each unit in the iteration, and the value ranges are all [0,1]]And follows the cauchy distribution;
n is 1,2 … N, and N represents the total number of units.
Figure BDA00030990466300000312
Satisfy the following relational expressions respectively:
Figure BDA00030990466300000313
Figure BDA00030990466300000314
Figure BDA00030990466300000315
wherein, Tt-1Represents the system temperature value of the previous round, when T is 1, Tt-1＝T0Namely the initial value of the temperature;
Figure BDA00030990466300000316
respectively are parameters randomly generated from uniform distribution of proportional parameters, integral parameters and differential parameters of each unit in the iteration, and the value ranges are all [0,1]]Sgn is a sign function, and its specific meaning is:
Figure BDA00030990466300000317
Figure BDA00030990466300000318
Figure BDA00030990466300000319
in step 6, the iteration stop criterion is to calculate Δ C (X)tLess than or equal to the loss value threshold. Δ C (X)tIs calculated by：
ΔC(X)t＝C(X)t-C(X)t-1
Wherein, C (X)tRepresents the loss value of the current iteration, C (X)t-1Representing the loss value of the previous iteration; when t is 1, C (X)t-1The value of (d) is 0.
The loss value threshold is 0.
In step 7, when the judgment factor r meets the following requirements, the current result is output:
Figure BDA0003099046630000041
wherein r is a parameter randomly generated from uniform distribution, and the value range is [0,1 ].
In step 8, the temperature parameter is updated to satisfy the following relation:
Figure BDA0003099046630000042
wherein, TtShows the updated result of the temperature parameter of the iteration of the current round, mgmaxFor the current iteration result matrix KtMaximum value of sensitivity coefficient of each parameter in (mg)meanFor the current iteration result matrix KtAverage value of the sensitivity coefficient of each parameter, in the invention, the current iteration result matrix KtSensitivity coefficient mg of the ith parameteriThe calculation method comprises the following steps:
Figure BDA0003099046630000043
Figure BDA0003099046630000044
compared with the prior art, the method for regulating and controlling the parameters of the adaptive regulator has the advantages that the loss model required by parameter optimization is established by considering various factors such as the output frequency, undershoot, establishment time and the like of each unit in the system, and the weights in the model are the optimal values which can enable the system to recover at the fastest speed and are obtained through a large number of experiments, so that the accuracy is higher; the parameter updating method in the self-adaptive control method considers the negative influence of random initialization parameter values on the optimization speed, and carries out simulation updating on temperature values in different states by calculating the sensitivity coefficient of the same type of parameters of different machines, thereby greatly reducing the dependence of the optimization of the parameters of the multi-machine speed regulator on the set initial value and improving the parameter updating speed; the invention also introduces a step of randomly generating a judgment factor to judge the optimizing result, and the judgment method provided in the step can avoid the problems that the self-adaptive algorithm of the invention falls into local optimization and the global optimizing capability is insufficient. The algorithm is a global optimal solution, but each round of updating of each parameter is solved according to the numerical value of the previous round of the parameter, so that the calculation speed is higher.
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FIG. 1 is a flow chart of a method for regulating and controlling the parameters of an adaptive multi-machine hydropower station speed regulator according to the invention;
FIG. 2 is a schematic diagram of updating of three unit proportional parameters and iteration times of the adaptive multi-machine hydropower station speed regulator parameter regulation method of the invention;
Detailed Description
The present application is further described below with reference to the accompanying drawings. The following examples are only for illustrating the technical solutions of the present invention more clearly, and the protection scope of the present application is not limited thereby.
FIG. 1 is a detailed flow chart of the present invention, a method for regulating and controlling adaptive multi-machine hydropower station speed regulator parameters, comprising the steps of:
step 1: collecting relevant parameters of each unit in a hydropower station power system;
the relevant parameters comprise the output frequency of each unit in the power system, the signal energy of a control signal, system undershoot and establishment time; the settling time refers to the time required by the unit from outputting an oscillating signal to the time when the signal tends to be stable, and the stability starting time is defined as the time when the amplitude change of the system output signal is maintained between 2% and 5%.
Step 2: constructing a loss model for controlling a hydropower station speed regulator of a plurality of machines;
the loss model constructed by the invention satisfies the following relations:
C(X)＝w1×SE+w2×SCS+w3×MU+w4×ST+w5×PI
wherein C (X) represents a loss value for controlling N units, w1、w2、w3、w4And w5Weights corresponding to SE, SCS, MU, ST and PI respectively; w is a1Has a value range of (0, 1)]，w2Is selected to be in the range of [500,1500 ]]，w3Is selected in the range of [50,150%]，w4Is selected to be in the range of [1000,5000]，w5The selected value range of [1000, ∞); in this embodiment, w1Is 1, w2Is 800, w3Is 100, w4Is 1000, w5Is 1000;
SE represents the sum of the difference between the output frequency of the N units and the reference frequency of 50 Hz;
SCS represents the square sum of the control signals of the N units, namely the signal energy of the control signals of the N units;
MU represents the sum of the N set undershoots;
ST represents the sum of the establishment time of N units;
PI represents punishment values of preventing overshoot of N units;
SE satisfies the following relation:
Figure BDA0003099046630000061
wherein, FQn(t) represents an output frequency of the nth system;
SSC satisfies the following relationship:
Figure BDA0003099046630000062
wherein CSn(NN) represents the control signal for the nth system, NN represents the total number of sample points;
Figure BDA0003099046630000063
wherein, DSnUndershoot representing the nth system;
Figure BDA0003099046630000064
wherein sgn is a sign function, and satisfies the following relationship:
Figure BDA0003099046630000065
and step 3: initializing the proportional parameters to be regulated and controlled in each unit
Figure BDA0003099046630000066
Integral parameter
Figure BDA0003099046630000067
Differential parameter
Figure BDA0003099046630000068
Forming an initialization matrix K0(ii) a Simultaneously initializing upper bound initial value of each unit proportion parameter
Figure BDA0003099046630000069
Lower bound initial value of the proportional parameter
Figure BDA00030990466300000610
Upper bound initial value of integration parameter
Figure BDA00030990466300000611
Lower bound initial value of integration parameter
Figure BDA00030990466300000612
Upper bound initial value of differential parameter
Figure BDA00030990466300000613
Lower bound initial value of differential parameter
Figure BDA00030990466300000614
Initial system temperature parameter T0N is 1,2 … N, N represents the total number of units;
forming an initialization matrix
Figure BDA00030990466300000615
The method for initializing the proportional parameters, the integral parameters and the differential parameters of each system can be random number generation, and can also be used for initializing the parameters of each system by using Gaussian distribution; the values of the proportional parameter, the integral parameter and the differential parameter of each initialized system are required to be between the corresponding upper and lower limits, and the upper limit is required to be larger than the lower limit.
Initial temperature T0The random generation can be carried out according to the actual situation, and the preferred value is [60, 80 ]]To (c) to (d);
and 4, step 4: updating the proportional parameter, the integral parameter and the differential parameter;
the updating method comprises the following steps:
Figure BDA0003099046630000071
Figure BDA0003099046630000072
Figure BDA0003099046630000073
wherein the content of the first and second substances,
Figure BDA0003099046630000074
the value of the proportional parameter after the current iteration is represented,
Figure BDA0003099046630000075
representing the value of the scale parameter obtained after the last iteration,
Figure BDA0003099046630000076
the values of the integrated parameters after this iteration are represented,
Figure BDA0003099046630000077
representing the value of the scale parameter obtained after the last iteration,
Figure BDA0003099046630000078
the value of the differential parameter after this iteration is represented,
Figure BDA0003099046630000079
representing the value of the differential parameter obtained after the last iteration, t representing the number of iterations, the initial value being 1,
Figure BDA00030990466300000710
respectively the intermediate quantities of the proportional parameter, the integral parameter and the differential parameter of each unit in the iteration, and the value ranges are all [0,1]]And follow the cauchy distribution, which respectively satisfy the following relationships:
Figure BDA00030990466300000711
Figure BDA00030990466300000712
Figure BDA00030990466300000713
wherein, Tt-1Represents the system temperature value of the previous round, when T is 1, Tt-1T0 is the initial value of the temperature,
Figure BDA00030990466300000714
respectively are parameters randomly generated from uniform distribution of proportional parameters, integral parameters and differential parameters of each unit in the iteration, and the value ranges are all [0,1]]Sgn is a sign function, and its specific meaning is:
Figure BDA00030990466300000715
Figure BDA00030990466300000716
Figure BDA0003099046630000081
and 5: inputting the proportional parameters, integral parameters and differential parameters calculated in the step 4 into the current system for simulation, collecting the relevant parameters of the system and calculating the loss values C (X) for controlling the N units during the current iterationt；
Step 6: judging whether the current loss value reaches an iteration stop standard, and if so, entering a step 7; otherwise, entering a step 8;
the iteration stop criterion in the present invention is to calculate Δ C (X)tLess than or equal to the loss value threshold.
ΔC(X)tThe calculation method comprises the following steps:
ΔC(X)t＝C(X)t-C(X)t-1
wherein, C (X)tRepresents the loss value of the current iteration, C (X)t-1Representing the loss value of the previous iteration; when t is 1, C (X)t-1Is 0;
in the present invention, a preferable value of the loss value threshold is 0.
And 7: randomly generating a judgment factor r from the uniform distribution, and if r meets the local optimization selection condition, outputting the current iteration result matrix
Figure BDA0003099046630000082
The final result of the method for regulating and controlling the parameters of the multi-machine hydropower station speed regulator is obtained; otherwise, entering a step 8;
in order to avoid the problems of local optimization and insufficient global optimization capability, the invention utilizes the following acceptance judgment method to determine whether the result obtained by current iteration is taken as the final result;
and when the judgment factor r meets the following requirements, outputting the current result:
Figure BDA0003099046630000083
wherein r is a parameter randomly generated from uniform distribution, and the value range is [0,1 ].
And 8: and updating the temperature parameters and returning to the step 4.
The updating of the temperature parameter in the invention satisfies the following relation:
Figure BDA0003099046630000084
wherein, TtShows the updated result of the temperature parameter of the iteration of the current round, mgmaxFor the current iteration result matrix KtMaximum value of sensitivity coefficient of each parameter in (mg)meanFor the current iteration result matrix KtAverage value of the sensitivity coefficient of each parameter, in the invention, the current iteration result matrix KtSensitivity coefficient mg of the ith parameteriThe calculation method comprises the following steps:
Figure BDA0003099046630000091
fig. 2 is a schematic diagram of updating the proportion parameters and the iteration times of three units of the self-adaptive multi-machine hydropower station speed regulator parameter regulating method, and as can be seen from the diagram, the optimal regulating value of the multi-machine parameters can be obtained after 20 iterations in the method.
The present applicant has described and illustrated embodiments of the present invention in detail with reference to the accompanying drawings, but it should be understood by those skilled in the art that the above embodiments are merely preferred embodiments of the present invention, and the detailed description is only for the purpose of helping the reader to better understand the spirit of the present invention, and not for limiting the scope of the present invention, and on the contrary, any improvement or modification made based on the spirit of the present invention should fall within the scope of the present invention.

Claims (11)

1. A self-adaptive multi-machine hydropower station speed regulator parameter regulating method comprises the following steps:
step 1: collecting relevant parameters of each unit in a hydropower station power system;
step 2: constructing a loss model for controlling a hydropower station speed regulator of a plurality of machines;
and step 3: initializing parameters to be regulated and controlled in each unit, and corresponding upper and lower bounds of the parameters and system temperature parameters; the parameters to be regulated comprise a proportional parameter, an integral parameter and a differential parameter;
and 4, step 4: updating the proportional parameter, the integral parameter and the differential parameter;
and 5: inputting the proportional parameter, integral parameter and differential parameter calculated in the step 4 into the current system for simulation, collecting the relevant parameters of the system and calculating the loss value C (X) of the current iterationt；
Step 6: judging whether the loss value of the current iteration reaches the iteration stop standard, if so, entering the step 7; otherwise, entering a step 8;
and 7: randomly generating a judgment factor r from the uniform distribution, and if r meets the local optimization selection condition, outputting a parameter result obtained by the iteration in the current round, namely a final result of parameter regulation and control of the multi-machine hydropower station speed regulator by the method; otherwise, entering a step 8;
and 8: and updating the temperature parameters and returning to the step 4.
2. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 1, wherein the method comprises the following steps:
in the step 1, the relevant parameters of each unit comprise the output frequency of each unit, the signal energy of a control signal, system undershoot and establishment time; the settling time refers to the time required by the unit from outputting an oscillating signal to the time when the signal tends to be stable, and the stability starting time is defined as the time when the amplitude change of the system output signal is maintained between 2% and 5%.
3. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 1 or 2, wherein the method comprises the following steps:
in step 2, the loss model is:
C(X)＝w1×SE+w2×SCS+w3×MU+w4×ST+w5×PI
wherein C (X) represents a loss value for controlling N units, w1、w2、w3、w4And w5Weights corresponding to SE, SCS, MU, ST and PI respectively;
SE represents the sum of the difference between the output frequency of the N units and the reference frequency of 50 Hz;
SCS represents the square sum of the control signals of the N units, namely the signal energy of the control signals of the N units;
MU represents the sum of the N set undershoots;
ST represents the sum of the establishment time of N units;
PI represents the penalty value of N teams to prevent overshoot.
4. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 3, wherein the method comprises the following steps:
said w1Has a value range of (0, 1)]W of2Is selected to be in the range of [500,1500 ]]W of3Is selected in the range of [50,150%]W of4Is selected to be in the range of [1000,5000]W of5Is [1000, ∞).
5. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 4, wherein the method comprises the following steps:
in the step 3, the method for initializing the proportional parameter, the integral parameter and the differential parameter of each system may be random number generation, or may use gaussian distribution to initialize the parameter of each system; the values of the proportional parameter, the integral parameter and the differential parameter of each initialized system are required to be between the corresponding upper and lower limits, and the upper limit is required to be larger than the lower limit.
6. The adaptive multi-machine hydropower station speed regulator parameter regulating method according to claim 3 or 4, wherein the method comprises the following steps:
in step 4, the updating method of the parameters includes:
Figure FDA0003099046620000021
Figure FDA0003099046620000022
Figure FDA0003099046620000023
wherein the content of the first and second substances,
Figure FDA0003099046620000024
the value of the proportional parameter after the current iteration is represented,
Figure FDA0003099046620000025
representing the value of the scale parameter obtained after the last iteration,
Figure FDA0003099046620000026
representing integral parameters after the iterationThe values of the number of the first and second,
Figure FDA0003099046620000027
representing the value of the scale parameter obtained after the last iteration,
Figure FDA0003099046620000028
the value of the differential parameter after this iteration is represented,
Figure FDA0003099046620000029
representing the differential parameter value obtained after the last iteration;
t represents the number of iterations, and the initial value is 1; then
Figure FDA00030990466200000210
The proportional parameter of the nth unit is shown,
Figure FDA00030990466200000211
represents the integral parameter of the nth unit,
Figure FDA00030990466200000212
representing a differential parameter of the nth unit;
Figure FDA00030990466200000213
respectively the intermediate quantities of the proportional parameter, the integral parameter and the differential parameter of each unit in the iteration, and the value ranges are all [0,1]]And follows the cauchy distribution;
n is 1,2 … N, and N represents the total number of units.
7. The method for regulating and controlling the parameters of the self-adaptive multi-machine hydropower station speed regulator according to claim 6, wherein the method comprises the following steps:
the above-mentioned
Figure FDA0003099046620000031
Satisfy the following relational expressions respectively:
Figure FDA0003099046620000032
Figure FDA0003099046620000033
Figure FDA0003099046620000034
wherein, Tt-1Represents the system temperature value of the previous round, when T is 1, Tt-1＝T0Namely the initial value of the temperature;
Figure FDA0003099046620000035
respectively are parameters randomly generated from uniform distribution of proportional parameters, integral parameters and differential parameters of each unit in the iteration, and the value ranges are all [0,1]]Sgn is a sign function, and its specific meaning is:
Figure FDA0003099046620000036
Figure FDA0003099046620000037
Figure FDA0003099046620000038
8. the method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 7, wherein the method comprises the following steps:
in step 6, the iteration stop criterion is to calculate Δ C (X)tIs required to be less than or equal to the lossA loss threshold. Δ C (X)tThe calculation method comprises the following steps:
ΔC(X)t＝C(X)t-C(X)t-1
wherein, C (X)tRepresents the loss value of the current iteration, C (X)t-1Representing the loss value of the previous iteration; when t is 1, C (X)t-1The value of (d) is 0.
9. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 8, wherein the method comprises the following steps:
the loss value threshold is 0.
10. The adaptive multi-machine hydropower station speed regulator parameter regulating method according to claim 8 or 9, wherein the method comprises the following steps:
in step 7, when the determination factor r satisfies the following requirements, the current result is output:
Figure FDA0003099046620000041
wherein r is a parameter randomly generated from uniform distribution, and the value range is [0,1 ].
11. The method for regulating and controlling the parameters of the self-adaptive multi-hydropower-station speed regulator according to claim 10, wherein the method comprises the following steps:
in step 8, the temperature parameter is updated according to the following relation:
Figure FDA0003099046620000042
wherein, TtShows the updated result of the temperature parameter of the iteration of the current round, mgmaxFor the current iteration result matrix KtMaximum value of sensitivity coefficient of each parameter in (mg)meanFor the current iteration result matrix KtAverage value of sensitivity coefficient of each parameter in the inventionIn, the current iteration result matrix KtSensitivity coefficient mg of the ith parameteriThe calculation method comprises the following steps:
Figure FDA0003099046620000043
Figure FDA0003099046620000044
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